dgpsi: Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations

Interface to the 'python' package 'dgpsi' for Gaussian process, deep Gaussian process, and linked deep Gaussian process emulations of computer models and networks using stochastic imputation (SI). The implementations follow Ming & Guillas (2021) <doi:10.1137/20M1323771> and Ming, Williamson, & Guillas (2023) <doi:10.1080/00401706.2022.2124311> and Ming & Williamson (2023) <doi:10.48550/arXiv.2306.01212>. To get started with the package, see <https://mingdeyu.github.io/dgpsi-R/>.

Version:	2.4.0
Depends:	R (≥ 4.0)
Imports:	reticulate (≥ 1.26), benchmarkme (≥ 1.0.8), utils, ggplot2, ggforce, reshape2, patchwork, lhs, methods, stats, bitops, clhs, dplyr, uuid
Suggests:	knitr, rmarkdown, MASS, R.utils, spelling
Published:	2024-01-14
DOI:	10.32614/CRAN.package.dgpsi
Author:	Deyu Ming [aut, cre, cph], Daniel Williamson [aut]
Maintainer:	Deyu Ming <deyu.ming.16 at ucl.ac.uk>
BugReports:	https://github.com/mingdeyu/dgpsi-R/issues
License:	MIT + file LICENSE
URL:	https://github.com/mingdeyu/dgpsi-R, https://mingdeyu.github.io/dgpsi-R/
NeedsCompilation:	no
Language:	en-US
Citation:	dgpsi citation info
Materials:	README NEWS
CRAN checks:	dgpsi results

Documentation:

Reference manual:	dgpsi.pdf
Vignettes:	A Quick Guide to dgpsi Linked (D)GP Emulation DGP Emulation with the Heteroskedastic Gaussian Likelihood Sequential Design I Sequential Design II

Downloads:

Package source:	dgpsi_2.4.0.tar.gz
Windows binaries:	r-devel: dgpsi_2.4.0.zip, r-release: dgpsi_2.4.0.zip, r-oldrel: dgpsi_2.4.0.zip
macOS binaries:	r-release (arm64): dgpsi_2.4.0.tgz, r-oldrel (arm64): dgpsi_2.4.0.tgz, r-release (x86_64): dgpsi_2.4.0.tgz, r-oldrel (x86_64): dgpsi_2.4.0.tgz
Old sources:	dgpsi archive

Linking:

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